HIVECOTEV1

class HIVECOTEV1(stc_params=None, tsf_params=None, rise_params=None, cboss_params=None, verbose=0, n_jobs=1, random_state=None)[source]

Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) V1.

An ensemble of the STC, TSF, RISE and cBOSS classifiers from different feature representations using the CAWPE structure as described in [1]. The default implementation differs from the one described in [1], in that the STC component uses the out of bag error (OOB) estimates for weights (described in [2]) rather than the cross validation estimate. OOB is an order of magnitude faster and on average as good as CV. This means that this version of HIVE COTE is a bit faster than HC2, although less accurate on average.

Parameters:

stc_paramsdict or None, default=None: Parameters for the ShapeletTransformClassifier module. If None, uses the default parameters with a 2 hour transform contract.
tsf_paramsdict or None, default=None: Parameters for the TimeSeriesForestClassifier module. If None, uses the default parameters with n_estimators set to 500.
rise_paramsdict or None, default=None: Parameters for the RandomIntervalSpectralForest module. If None, uses the default parameters with n_estimators set to 500.
cboss_paramsdict or None, default=None: Parameters for the ContractableBOSS module. If None, uses the default parameters.
verboseint, default=0: Level of output printed to the console (for information only).
n_jobsint, default=1: The number of jobs to run in parallel for both fit and predict. -1 means using all processors.
random_stateint or None, default=None: Seed for random number generation.

Attributes:

n_classes_int: The number of classes.
classes_list: The unique class labels.
stc_weight_float: The weight for STC probabilities.
tsf_weight_float: The weight for TSF probabilities.
rise_weight_float: The weight for RISE probabilities.
cboss_weight_float: The weight for cBOSS probabilities.

Notes

For the Java version, see `https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/ tsml/classifiers/hybrids/HIVE_COTE.java`_.

References

[1] (1,2)

Anthony Bagnall, Michael Flynn, James Large, Jason Lines and Matthew Middlehurst. “On the usage and performance of the Hierarchical Vote Collective of Transformation-based Ensembles version 1.0 (hive-cote v1.0)” International Workshop on Advanced Analytics and Learning on Temporal Data 2020

[2]

Middlehurst, Matthew, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, and Anthony Bagnall. “HIVE-COTE 2.0: a new meta ensemble for time series classification.” Machine Learning (2021).

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X, y)	Fit time series classifier to training data.
`fit_predict`(X, y[, cv, change_state])	Fit and predict labels for sequences in X.
`fit_predict_proba`(X, y[, cv, change_state])	Fit and predict labels probabilities for sequences in X.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Predicts labels for sequences in X.
`predict_proba`(X)	Predicts labels probabilities for sequences in X.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(X, y)	Scores predicted labels against ground truth labels on X.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.